Motion compensation is applicable to a wide variety of image processing tasks. In a motion compensated process, successive images in a sequence of images are compared and the differences between the positions of portrayed objects or image features between succeeding images are evaluated and assigned as respective motion vectors applicable to those objects or image features. Motion vectors can be used to combine image information from different images in the sequence without creating ‘multiple image’ artefacts. Typically, succeeding images in a sequence correspond to different temporal samples of a scene, such as film frames or interlaced video fields. However, motion compensation is equally applicable to other image sequences, for example views of a common scene having different viewpoints spaced along a path.
Historically the development of motion compensated video processing has concentrated on processing interlaced television images with temporal sampling rates (i.e. field frequencies) of 50 Hz and above. More recently developments in high definition television and digital cinematography have led to the development of motion compensated processes intended for temporal sampling rates around 24 Hz. At these lower rates the magnitudes of motion vectors are correspondingly greater and the process of motion estimation, in which motion vectors are evaluated, becomes more difficult. The low temporal sampling rate results in large differences between the positions of the same object in succeeding images, and the control of the depth of field for artistic reasons makes it difficult to determine the exact positions of some objects.
Hierarchical methods of motion estimation have been proposed, in which the result of a low-resolution, wide-range motion estimation process is refined according to the result of a higher-resolution, narrower-range process; and that process may itself be refined a number of times. In theory this enables accurate motion vectors to be derived for large inter-image positional differences.
However, these methods are complex to implement, especially if the hierarchy comprises many levels.
The current disclosure teaches techniques to improve motion compensated processing.